Urban Land Surface Research Articles

A cross-scale indicator framework for the study of annual stability of land surface temperature in different land uses

Urban Land Surface Temperature (LST) is crucial in surface urban heat island (SUHI) and microclimate studies. Currently, research has focused on seasonal LST differences across land uses, but annual LST fluctuations (ΔLST) within the same land use and their drivers remain underexplored. To explore the impact of land characteristics and urban elements on seasonal LST differences, we propose annual LST stability. We constructed a new indicator framework based on Land Use and Land Cover (LULC), supplemented by Land Morphology (LM) and Land Properties (LP), for cross-scale ΔLST research. We identified land use ratios and key characteristics of urban plots with high stability. The results show an interactive effect of the green land ratio to other land on ΔLST. For residential and office land, the green space ratio (GSR) is key to annual LST stability. Residential land needs a GSR of more than 24 %. The floor area ratio (FAR) for residential and office land has a significant nonlinear effect on annual LST stability, with FARs of 1.8 for residential land and 1.5 for office land being most detrimental to the LST stability. For practical implications, we conducted cluster analyses on residential, office, and green lands, providing strategies to improve stability. These conclusions help balance land economic benefits with urban climate resilience and guide urban planning and design to address the challenges of heat and cold waves.

The effect of urban form parameters on annual and diurnal cycles of land surface temperature with 30-meter hourly resolution

Understanding dynamic changes in urban land surface temperature (U-LST) is vital for improving urban thermal environment. Previous studies noted a close association between two/three-dimensional (2D/3D) urban form parameters (UFPs) and U-LST, but divergent conclusions emerged due to study scale and data spatiotemporal resolution. This study investigated the effect of 2D/3D UFPs on multi-temporal (hourly, diurnal, and annual) U-LSTs with 30-m spatial resolution, which is the first exploration of such dynamics at this fine scale. In addition, the influence of local climate zones (LCZs) was considered. Results showed that 2D/3D vegetation UFPs have the greatest summer cooling effect at 21:00, while 3D building UFP sky view factor (SVF) impacts minimum nighttime temperature more (5:00). The independently total explained variances (R2) of 2D UFPs (0.2–0.7) on seasonal average U-LSTs were higher than that of 3D UFPs (0.1–0.4) in most LCZs. LCZ 3 and LCZ 2 had the highest seasonal and daytime U-LST, respectively. Interestingly, SVF replaced normalized difference vegetation index as the dominant factor in LCZs 4–6 and LCZs A-C after summer. Findings of this study are useful for guiding urban planning, formulating urban heat island mitigation policies, and promoting sustainable development goals (SDGs 11, 13, 15) of cities and communities.

The marginal effect of landscapes on urban land surface temperature within local climate zones based on optimal landscape scale

The rising urban land surface temperature (LST) has undoubtedly caused an imminent threat to human habitat. Landscape patterns are among the most significant factors related to LST. However, the marginal effects and thresholds of landscape at optimal grid scale on LST within diverse local climate zones (LCZs) are poorly understood. This study employed the boosted regression trees (BRT) model to evaluate the relative influences and marginal effects of built-up, water, and green landscape metrics on LST at the optimal grid scale across various LCZs. The findings reveal that: (1) The spatial spread trend of LST closely matches the expansion trend of built-up landscapes. (2) Significant variations in average summer LST were observed among LCZs, with LCZ8 (large low-rise) exhibiting the highest temperatures and LCZ11 (dense trees) the lowest. (3) At the optimal scale of 1000 m, the DIVISION metric for water landscapes significantly influenced LCZ6 (open low-rise), LCZ8, and LCZ14 (low plants). (4) In LCZ6 and LCZ8, effective heat mitigation requires increasing water area to 50% and optimizing the water body separation index to 0.88. These findings suggest that heterogeneous landscape heat mitigation strategies should be considered across diverse LCZs in urban areas.

Spatial effect of urban morphology on land surface tempature from the perspective of local climate zone

Urban expansion results in the intrusion of artificial surfaces into natural environments, giving rise to phenomena such as urban heat islands and heightened temperatures within inhabited areas. This study examined the dynamic evolution of urban morphology and land surface temperature through the lens of local climate zone. Furthermore, the spatial relationship between urban morphology indices and land surface temperature was analyzed within the context of loops. The investigation yielded several noteworthy findings: (1) LCZ8, LCZ6, and LCZD were the most significant transfer-in and transfer-out classes within the study area. (2) The heat contribution of residential LCZs (LCZ 2,3,5) decreased over time, ranging from 0.415 to 0.171, while that of industrial LCZs (LCZ 8,10) increased within the range of 0.348–0.553. (3) Urban heat island mitigation can be achieved by regulating building height and surface fraction. Establishing a judicious threshold for reducing LST is imperative for sky view factor and impervious surface fraction factors. Surface albedo exhibited a substantial inhibitory effect in 2020. The observation of urban morphological changes and long-term local climate zone considerations integrate the responsibility of mitigating urban heat islands into urban planning.

Seasonal dynamics of land surface temperature and urban thermal comfort with land use land cover pattern in semi-arid Indian cities: Insights for sustainable Urban Management

The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC) pattern and seasonal variation in urban thermal comfort (UTC) and land surface temperature (LST). This study examines the seasonal variation in LST and UTC and quantify the importance of LULC pattern in influencing LST in eight Indian semi-arid cities using Landsat 8 datasets. Random forest regression (RFR) model has been applied to quantify the influence of LULC in determining LST. The study shows that bare soil and open spaces in outskirts of selected cities have comparatively high LST than the core of cities. The mean LST varies seasonally from 12.84 °C in Udaipur to 26.18 °C in Jaipur in winter and summer. Analysis of UTC shows that autumn and winter seasons have better UTC than spring and summer seasons. RFR analysis shows a robust correlation (R2 > 0.85) between LULC and LST across all cities. Notably, built-up areas, open land, and vegetation cover exert the greatest influence on LST. Study suggests that targeted LULC planning may effectively address UTC problem in semi-arid cities and enhance sustainability. Study may help in understanding the seasonality thermal environment and assist in mitigating UHI and maintain UTC in these cities.

Detailed height mapping of trees and buildings (HiTAB) in Chicago and its implications to urban climate studies

Abstract The vertical dimensions of urban morphology, specifically the heights of trees and buildings, exert significant influence on wind flow fields in urban street canyons and the thermal environment of the urban fabric, subsequently affecting the microclimate, noise levels, and air quality. Despite their importance, these critical attributes are less commonly available and rarely utilized in urban climate models compared to planar land use and land cover data. In this study, we explicitly mapped the height of trees and buildings (HiTAB) across the city of Chicago at 1 m spatial resolution using a data fusion approach. This approach integrates high-precision light detection and ranging (LiDAR) cloud point data, building footprint inventory, and multi-band satellite images. Specifically, the digital terrain and surface models were first created from the LiDAR dataset to calculate the height of surface objects, while the rest of the datasets were used to delineate trees and buildings. We validated the derived height information against the existing building database in downtown Chicago and the Meter-scale Urban Land Cover map from the Environmental Protection Agency, respectively. The co-investigation on trees and building heights offers a valuable initiative in the effort to inform urban land surface parameterizations using real-world data. Given their high spatial resolution, the height maps can be adopted in physical-based and data-driven urban models to achieve higher resolution and accuracy while lowering uncertainties. Moreover, our method can be extended to other urban regions, benefiting from the growing availability of high-resolution urban informatics globally. Collectively, these datasets can substantially contribute to future studies on hyper-local weather dynamics, urban heterogeneity, morphology, and planning, providing a more comprehensive understanding of urban environments.

An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems

Abstract. Global urban expansion has altered surface aerodynamics and hydrothermal dynamics, aggravating environmental challenges such as urban heat and urban dry islands. To identify such environmental responses, various physical models, including urban canyon models (UCMs) and land surface models (LSMs), have been developed to represent surface hydrothermal processes. However, UCMs often treat a city as a unified entity and overlook subcity heterogeneity. LSMs are generally designed for natural land cover types and lack the capability to capture urban characteristics. To address these limitations, the aim of this study is to couple an urban module with a sophisticated LSM, i.e. the Variable Infiltration Capacity (VIC) model. This coupled model, i.e. the VIC-urban model, is characterized by its ability to coordinate certain critical urban features, including urban geometry, radiative interactions, and human impacts. Adopting Beijing as an evaluation site, the VIC-urban model shows higher performance than the original version, with excellent accuracy in simulating sensible heat, latent heat, runoff, and land surface temperature (LST). The absolute error is smaller than 25 % for the sensible heat and latent heat and smaller than 12 % and 30 % for the LST and runoff, respectively, which indicates that VIC-urban can effectively simulate hydrological and thermal fluxes in urban systems. Sensitivity analysis reveals that the roof emissivity and interception capacity exert the greatest impact on the roof temperature and evaporation and the height-to-width ratio has the greatest influence on the canyon. Our work introduces a reliable option for large-scale land surface simulations that accounts for urban environments and is among the first attempts to establish a systematic urban modelling framework of the VIC model. The VIC-urban model enables the analysis of urbanization-induced environmental changes and quantification of environmental variations among different urban configurations. The proposed model can thus offer invaluable insights for urban planners and landscape designers.

Spatiotemporal heterogeneity of the relationship between urban morphology and land surface temperature at a block scale

Understanding the relationship between urban morphology and land surface temperature (LST) is essential for mitigating urban heat island (UHI). This study investigates the spatiotemporal heterogeneity of the relationship between urban morphology and LST across 5 experiment sites located in different temperature zones of China at a block scale. Normalized difference vegetation index (NDVI), building density (BD), floor area ratio (FAR), average building height (ABH) and open space ratio (OSR) are adopted to indicate the physical urban morphology. The results show positive relationship between BD and LST, and negative relations between LST and NDVI, ABH and OSR from a global perspective. The relations of FAR to LST are mixed. However, variable relationship between urban morphological indicators (UMIs) and LST are significantly observed at a block scale. 12 scenes that generate local relations of UMIs and LST differing from that of the surroundings are identified. Typical strategies for land development, vegetation phenology, large natural elements and human activities may be the most important causative factors to the heterogenous relationship between urban morphology and LST. Findings derived in the study would promote studies on mechanisms of the effects of urban morphology on LST and contribute to practices of UHI mitigation.

Modelling future land use land cover changes and their impacts on urban heat island intensity in Guangzhou, China

During rapid urbanization in developing countries, changes in land use and land cover (LULC) can significantly alter urban land surface temperatures (LST), exacerbating the urban heat island (UHI) effect and degrading the outdoor environment. In this study, taking Guangzhou, China, as an example, we used Landsat series satellite data from 1992 to 2022, classified the LULC of the study area by the Support Vector Machine (SVM) method, estimated the LST of the area by the mono-window algorithm, and classified the LST of the study area into five UHI intensity classes based on the normalized values of the LST, and explored the influence of the LULC on the distribution of the UHI intensity. The CA-ANN (cellular automata-artificial neural network) model in QGIS software was employed to forecast the distribution of LULC and UHI intensity in Guangzhou for 2032. The findings reveal a strong correlation between UHI intensity and LULC, with water bodies and vegetation primarily exhibiting low and sub-low temperatures, while urban areas exhibit sub-high and high temperatures. The prediction results show that, according to the current development trend, compared with 1992, the water body and vegetation cover in 2032 will decrease by 46.97% and 34.24%, the building land will increase by 263.71%, and the sub-high and high temperature areas will increase by 127.76% and 375.92%. By analysing the spatial and temporal changes in LULC and its relationship with the distribution of UHI intensity during urbanization, this study assists government administrations and urban planners in devising sensible urban development strategies and implementing effective measures to plan LULC rationally. This approach aims to mitigate the impacts of the urban heat island and foster sustainable urbanization.

Influence of color steel buildings on urban land surface temperature: a case study in Urumqi, Xinjiang, China

Influence of color steel buildings on urban land surface temperature: a case study in Urumqi, Xinjiang, China

Investigating the spatial interaction between urban expansion and the regional thermal environment in Guangxi Beibu Gulf urban agglomeration of China

Rapid urbanization and its associated surface modifications play a vital role in generating and intensifying the urban heat island (UHI). Numerous research has investigated the impact of landscape composition and structure on urban land surface temperature (LST), yet the spatial clustering and heterogeneity between urbanization and the regional thermal environment remained largely unexplored. Here, we analyzed land use dynamics and LST variations in the Guangxi Beibu Gulf urban agglomeration (GBG_UA) from 2000 to 2020. The spatial relationships between urban expansion and the regional thermal environment were explored by bivariate spatial autocorrelation and coupling coordination degree analysis. The results demonstrated that the land use in the GBG_UA remained relatively stable, except for the expansion of developed land in urban centers. The area of heat island zones clearly increased, leading to an enhancement of the UHI effect. Urban development and the regional thermal environment exhibited a robust positive spatial autocorrelation. The H–H (High-High) and L-L (Low-Low) types displayed significant aggregation effects. The H–H cluster areas dominated the central urban cores of prefecture-level cities, while the L-L correlation areas were primarily found in mountainous regions. The overall coupling coordination degree was relatively low, with most areas falling into the moderate dissonance level. While it showed a positive shift, with the regions of concordance levels expanding and coupling coordination degree increased. Our study reveals a close spatial correlation between urban sprawl and the UHI effect. The results can offer valuable insights for optimizing the urban green infrastructure to enhance urban heat mitigation and adaptation.

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification

The Heterogeneous Effects of Microscale-Built Environments on Land Surface Temperature Based on Machine Learning and Street View Images

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents’ daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors’ impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development.

Towards inclusive urbanism: An examination of urban environment strategies for enhancing social equity in Chengdu's housing zones

With rapid urbanization, the urban heat island (UHI) effect has become a major problem worldwide. This study focuses on the relationship between Urban Green Space (UGS) and Land Surface Temperature (LST), providing insights into how natural environment, urban construction, and human activities influence this relationship by analyzing housing areas of different tiers. Utilizing the LightGBM machine learning approach, this study takes Chengdu City in Southwest China as a research area and finds that green space has a more significant cooling effect on LST in the middle-market housing and affordable housing areas. Furthermore, within the NDVI range of 0.37 to 0.48, the LST decreases by 0.63℃ for every 0.1 increase. Additionally, the study reveals that urban construction characteristics, such as building density and building height, exert a more significant influence on LST in middle-market housing and affordable housing areas, with a contribution rate of 23.61 % observed in middle-market housing areas and the lowest contribution rate of 17.84 % observed in high-end housing areas. Overall, our research emphasizes the importance of addressing urban thermal disparities, particularly in diverse housing areas, by prioritizing green space augmentation and optimizing built environments, thereby offering actionable insights for equitable and sustainable urban planning.

Linkages between urban growth and land surface temperature variations in the Seoul metropolitan area: A spatial first-order difference approach

This study investigates the effects of urban development and expansion on Land Surface Temperature (LST) changes from 2000 to 2018, using the Seoul Metropolitan area (SMA) as a case study area. Along with descriptive analysis, this study uses a spatial first-order difference model to statistically analyze the longitudinal relationship between land use features and LST, including impervious surface (IS), enhanced vegetation index (EVI), anthropogenic factors, and urban spatial and topographical factors as well as urban development types as influential factors on LST variations. Our findings support prior research indicating the cooling effects of vegetation and warming effects of imperviousness. We also found a positive association between detached housing units, manufacturing activities and LST changes and a negative relationship between apartment units, urban parks, service activities and LST changes. More importantly, substantial LST reduction was found in the large-scale high-rise apartment complexes redeveloped from the low-rise, detached housing sites in inner-city infill areas, contrasting with significant LST rise in the suburban new town development. We suggest redevelopment of inner-city low-rise compact areas into high-rise but greener blocks as a LST mitigation strategy, equipped with cooling strategies such as green infrastructure and climate-sensitive design considering local microclimate and natural ventilation.

Spatiotemporal assessment of the nexus between urban sprawl and land surface temperature as microclimatic effect: implications for urban planning.

Rapid urbanisation has led to significant environmental and climatic changes worldwide, especially in urban heat islands where increased land surface temperature (LST) poses a major challenge to sustainable urban living. In the city of Abha in southwestern Saudi Arabia, a region experiencing rapid urban growth, the impact of such expansion on LST and the resulting microclimatic changes are still poorly understood. This study aims to explore the dynamics of urban sprawl and its direct impact on LST to provide important insights for urban planning and climate change mitigation strategies. Using the random forest (RF) algorithm optimised for land use and land cover (LULC) mapping, LULC models were derived that had an overall accuracy of 87.70%, 86.27% and 93.53% for 1990, 2000 and 2020, respectively. The mono-window algorithm facilitated the derivation of LST, while Markovian transition matrices and spatial linear regression models assessed LULC dynamics and LST trends. Notably, built-up areas grew from 69.40 km2 in 1990 to 338.74 km2 in 2020, while LST in urban areas showed a pronounced warming trend, with temperatures increasing from an average of 43.71°C in 1990 to 50.46°C in 2020. Six landscape fragmentation indices were then calculated for urban areas over three decades. The results show that the Largest Patch Index (LPI) increases from 22.78 in 1990 to 65.24 in 2020, and the number of patches (NP) escalates from 2,531 in 1990 to an impressive 10,710 in 2020. Further regression analyses highlighted the morphological changes in the cities and attributed almost 97% of the LST variability to these urban patch dynamics. In addition, water bodies showed a cooling trend with a temperature decrease from 33.76°C in 2000 to 29.69°C in 2020, suggesting an anthropogenic influence. The conclusion emphasises the urgent need for sustainable urban planning to counteract the warming trends associated with urban sprawl and promote climate resilience.

Exploring Land Use/Land Cover Dynamics and Statistical Assessment of Various Indicators

Current information on urban land use and surface cover is derived from the land classification of cities, facilitating accurate future urban planning. Key insights are driven by multi-year remote sensing data. These data, when analyzed, produce high-resolution changes on the Earth’s surface. In this context, publicly accessible Urban Atlas data are employed for the high-precision and high-resolution classification and monitoring of terrestrial surfaces. These datasets, which are useful for preserving natural resources, guiding spatial developments, and mitigating pollution, are crucial for monitoring changes and managing cities. This research aims to analyze and contrast land use and land cover (LULC) changes in Gaziantep (Turkey) between 2010 and 2018 using Urban Atlas data, and to investigate correlations between the city’s statistical data and LULC changes. Gaziantep’s urban dynamics were analyzed using Urban Atlas datasets from 2010 to 2015 and 2012 to 2018, the latter part of Copernicus, the European Earth Observation Programme. To understand the impact of LULC changes on urban landscapes, people, and the environment, official environmental and demographic statistics spanning four years were sourced and studied. The findings reveal a trend of agricultural and vacant lands evolving into residential and industrial zones, with such changes likely to increase in the near future, given the growth of building zones. While some land classes have shown consistent area values annually, residential and industrial zones have expanded in response to housing and employment demands. The most significant alterations have occurred in the last three years. Shifts in urban configurations align closely with migratory patterns, reflecting notable variations in factors like population, consumption, and pollution.

Spatio-temporal urban land surface temperature variations and heat stress vulnerability index in Thiruvananthapuram city of Kerala, India

ABSTRACT Land use/Land cover (LU/LC) modifications leads to rise in surface temperature is a global concern. This study investigates the effects of LU/LC changes in Land Surface Temperature (LST) variations and effects of Urban Heat Island (UHI) in Thiruvananthapuram city. Furthermore, the study proposed a novel heat stress vulnerability index (HSVI) using Analytical Hierarchy Process (AHP) method. Results shows that transformation of all land uses is to built-up areas, which shows an increase of +118.46% where as others such as vegetation (-24.27%), open area (-64.315) and waterbody (-43.88%) shows drastic decrease over 34 years. The relationship between Normalized Difference Vegetation Index and LST shows a significant change with R2 values between 0.223 in 1988, 0.382 in 2000 and 0.253 in 2022. Moreover, this also contributed to the increase of the mean LST of 26.96 ºC in 1988 to 28.52 ºC in 2022. HSVI of the study indicates that 30.83% of the study area is categorized as very low heat stress vulnerable area followed by 37.22% area in low heat stress vulnerability, 17.88% in moderate heat stress vulnerability, 8.87% in high heat stress vulnerability and 5.19% of the area falling in very high heat stress Vulnerability class.

The anisotropy of MODIS LST in urban areas: A perspective from different time scales using model simulations

Remote sensing is one of the effective means to obtain urban land surface temperature (LST), but the observed temperature varies with sensor viewing angle due to urban thermal anisotropy (UTA) and biased sensor viewing angle. The anisotropy of satellite-based LST products (e.g., MODIS LST) varies at different time scales. Previous researches focus on the anisotropy of MODIS LST at the seasonal scale or at other time scales (e.g., hourly) but only for individual cities. The characteristics of anisotropy at different time scales and for cities at different latitudes are still unknown. In this study, we focus on analyzing the characteristics of anisotropy at hourly, daily, seasonal, and annual time scales separately for 80 global cities using the simulation data. The simulation data were generated by coupling a microscale urban surface temperature model and a thermal remote sensing model. In addition, the impact factors (i.e., sensor factor, meteorological factor, and surface factor) at different time scales are discussed and validated using satellite observations. The results show that the daytime instantaneous anisotropy varies from −4.2 K to 1.1 K at the hourly scale. For each city, the smaller anisotropy is captured at the local time 12:00 more easily for both Terra and Aqua overpass during daytime. This is because the magnitude of urban thermal anisotropy is more influenced by the sensor factor (i.e., sensor zenith angle), which varies with the overpass time and the relative position of the city in the track. However, if the seasonally or yearly aggregated MODIS LST is utilized to study mean surface temperature, the intensity of angular effect is much smaller with the value ranging from 0.1 K to 3.3 K at the seasonal scale (from 0.2 K to 2.1 K at the yearly scale) because the variation of sensor zenith angle is smoothed. At seasonal and yearly scales, the strength of the angular effect is greater in the morning during daytime, in summer among all seasons, and in the cities with lower latitudes, which is due to larger solar radiation. Among all meteorological factors, wind speed also has a larger impact on the magnitude of surface thermal anisotropy besides the downward surface shortwave radiation. Our study provides a reference for the correction of the angular effect in the MODIS LST products.

Quantifying the Impact of Urban Growth on Urban Surface Heat Islands in the Bangkok Metropolitan Region, Thailand

The urban built environment, comprising structures, roads, and various facilities, plays a key role in the formation of urban heat islands, which inflict considerable damage upon human society. This phenomenon is particularly pronounced in urban areas characterized by the rapid growth and concentration of populations, a global trend, notably exemplified in megacities such as Bangkok, Thailand. The global trend of urbanization has witnessed unprecedented growth in recent decades, with cities transforming into megametropolises that profoundly impact changes in urban temperature, specifically the urban heat island (UHI) phenomenon induced by the rapid growth of urban areas. Elevated urban concentrations lead to increased city density, contributing to higher temperatures within the urban environment compared to the surrounding areas. The evolving land-use surface has assumed heightened significance due to urban development, necessitating accelerated efforts to mitigate urban heat islands. This study aims to quantify the influence of urban growth on urban surface temperature in Bangkok and its surrounding areas. The inverse relationship between urban temperature and land surface temperature (LST), coupled with urban area density, was examined using Landsat 5 and 8 satellite imagery. The analysis revealed a positive correlation between higher temperatures and levels of urban growth. Areas characterized by high-rise structures and economic activities experienced the most pronounced impact of the heat island phenomenon. The city exhibited a notable correlation between high density and high temperatures (high–high), signifying that increased density contributes to elevated temperatures due to heat dissipation (significant correlation of R2 = 0.8582). Conversely, low-temperature, low-density cities (low–low) with a dispersed layout demonstrated effective cooling of the surrounding area, resulting in a significant correlation with lower local temperatures (R2 = 0.7404). These findings provide valuable insights to assist governments and related agencies in expediting planning and policy development aimed at reducing heat in urban areas and steering sustainable urban development.